Reprint
“Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development
Edited by
April 2024
316 pages
- ISBN978-3-7258-0818-2 (Hardback)
- ISBN978-3-7258-0817-5 (PDF)
This book is a reprint of the Special Issue “Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development that was published in
Biology & Life Sciences
Chemistry & Materials Science
Environmental & Earth Sciences
Summary
The aim of this Special Issue is to explore and support the evolution of emerging digital technology applications in agriculture and biology, including but not limited to agriculture, data collection, data mining, bioinformatics, genomics, and phenomics, as well as applications of machine learning and artificial intelligence. The development of a community to support this goal requires the cross-linking and integration of multiple sources of agricultural research across 3S technologies (remote sensing—RS; geographic information systems—GIS; global positioning systems—GPS).
Format
- Hardback
License
© 2024 by the authors; CC BY-NC-ND license
Keywords
corn seeds; image identification; multi-scale feature fusion; deep learning; machine vision; improved DeepLabV3+; attention mechanism; image segmentation; strawberry; weed identification; Faster-R-CNN; FPN; ResNeXt; attention mechanism; improved DeepLabv3+ model; semantic segmentation; transformer; weed recognition; appearance quality identification of ginseng; deep learning; attention mechanism; activation function; loss function; panchagavya; organic fertilizer; liquid fertilizer; automated fertilizer production; drip irrigation system; automated irrigation; soil texture; identification; DLAC-CNN-RF model; accuracy; laser heterodyne radiometer; carbon dioxide; methane; nitrous oxide; field measurement; pest identification; FCN; DenseNet; attention mechanism; deep learning; attention mechanism; maize leaf disease; digital agriculture; seed metering device; monitoring system; photoelectric sensor; miss; multiples; flow rate; smart agriculture; citrus diseases; generative adversarial network; classification network; FastGAN; EfficientNet; septoriosis; Septoria tritici blotch; hyperspectral signature; hyperspectral disease detection; data science; neural network; wheat; seed vigor; spectral detection technology; image detection technology; digital agriculture; Information communication technology; agriculture; ensemble learning; Gaussian probabilistic method function; convolutional neural network; support vector machines; crop phenotype; maize; stem diameter; morphological gradient; target region; YOLOv7-tiny-Apple; small target; fruit detection and counting; digital agriculture; crop seedling detection; dense target detection; lightweight transformer; YOLOv5; edible fungi fruit body; disease recognition; ShuffleNetV2; attention mechanism; spatial data quality; smart agriculture; data quality assessment; data quality dimensions; interpolation; classification; n/a